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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Saeedeh Shekari S. Ali A. Moosavian Dept of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab K. N. Toosi University of Technology Tehran Iran

Modeling the dynamics of continuum robotic arms is challenging due to the highly nonlinear nature and uncertain and complicated inherent structure. This affects the application of continuum arms in various aspects, such as inverse kinematics, path generation, optimization, and control. This paper deals with the dynamics modeling of continuum robotic arms using screw theory to develop a suitable model with high accuracy, speed, and low computational load to achieve real-time control. By using the representation of the Lie group of screws in the screw theory, which simplifies the mathematics of the problem, it is possible to achieve a real-time integrated representation of the dynamics of the deformation of the body of continuum robotic arms. This provides the possibility of accurately predicting the system behavior even in the presence of external forces acting on the end-effector. The experimentally validated results for circular and infinite motion trajectories show the model effectiveness in enhancing the trajectory tracking performance and ability to estimate configuration shape changes under external loads. The integration of this model in soft robotic arms has the potential to revolutionize their application in a wide range of industries and medical applications.

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A Data-Driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat

A Data-Driven Method for Estimating Formation Flexibility in...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Edvards Scukins Andre N. Costa Petter Ögren Aeronautical Solutions Division SAAB Aeronautics Robotics Perception and Learning Lab. Royal Institute of Technology (KTH) Decision Support Systems Subdivision Institute for Advanced Studies (IEAv)

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a high-performance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.

关键词： Training Measurement Missiles Visualization Atmospheric modeling Decision making Artificial neural networks

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Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 33-50页

作者： Jinghua Xu Kunqian Liu Linxuan Wang Hongshuai Guo Jiangtao Zhan Xiaojian Liu Shuyou Zhang Jianrong Tan State Key Lab of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310058China Key Lab of Advanced Manufacturing Technology of Zhejiang Province Zhejiang UniversityHangzhou 310058China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang UniversityHangzhou 310058China Zhejiang-Singapore Innovation and AI Joint Research Lab Zhejiang UniversityHangzhou 310058China School of Creative Arts and design Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou 310053China Ningbo Research Institute Zhejiang UniversityNingbo 315100China

This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for *** implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic *** thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical *** electrothermal experiment was performed by measuring the temperature and changes in temperature during *** thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the ***,manufacturability was verified based on a thermal-solid coupled finite element *** physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

关键词： Robustness optimization design Rapid prototyping Functional artifacts Fuzzy decision-making Infrared thermographs Visualized computing digital twins

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Design and Manufacturing of an Intelligent Ping-Pong Robot (RoboPing)

Design and Manufacturing of an Intelligent Ping-Pong Robot (...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian Arian D. Farahani Ali Karimi Advanced Robotics and Automated Systems (ARAS) Lab. Dept of Mechanical Engineering Center of Excellence in Robotics and Control K. N. Toosi University of Technology Tehran Iran

robotics and artificial intelligence (AI) are rapidly transforming many industries and aspects of our lives, and sport activities are no exception. Robots and AI are already being used in a variety of sports, including training and performance analysis. This paper introduces a new assistant ping-pong robot (RoboPing) that shoots ping-pong balls with different speeds and spins, besides using AI to analyze training sessions and providing useful insights into the game. RoboPing represents a significant advancement in mechanical design over previous versions, offering enhanced functionality. Being equipped with a precise vision system makes RoboPing more advanced compared to existing versions.

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Test-Stand Experimental Analysis of RoboWalk Weight Assisting Performance

Test-Stand Experimental Analysis of RoboWalk Weight Assistin...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Morteza Ghafoori S. Ali A. Moosavian Dept of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab. K. N. Toosi University of Technology Tehran Iran

The increment in elder population has been the main cause of exoskeletons and rehabilitation robots development. Studies have proven that assistive robots are more effective in limb empowerment and rehabilitation than traditional ways. A lower-limb assistive robot called RoboWalk has been designed in ARAS lab at K. N. Toosi University of technology to help compensate user weight when moving. This study reviews the RoboWalk weight assisting robot and illustrates its Test-Stand more thoroughly with updated modifications and parts. Test-Stand system features and operational specifications are then described. Then, system kinetics and kinematics are also explained. Finally, the weight assisting performance of RoboWalk is analyzed. Results show that the gait generating mechanism creates a singularity in Test-Stand system that causes a decrease in the assisting force. It is concluded that this issue is only related to the Test-Stand and does not appear in the actual RoboWalk robot. Nevertheless, Solutions to avoid this in Test-Stand including back and forth simulation of the gait and maximum motor torque limitation are also suggested.

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Walking Control for a Self-Balancing Exoskeleton on Flat Ground

Walking Control for a Self-Balancing Exoskeleton on Flat Gro...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jinke Li Dingkui Tian Feng Li Jianquan Sun Meng Yin Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China SIAT CUHK Joint Laboratory of Robotics and Intelligent Systems

Designed to provide rehabilitation or the assistance of walking for individuals who are with lower limb muscle injuries or spinal during their activities, self-balancing lower limb exoskeletons have been developed. This paper introduces an innovative controller for a fully actuated exoskeleton that ensures stable walking for wearers without the need for external devices. The exoskeleton robot is represented as a center of mass model, and based on this representation, the trajectory generation and controller are discussed, aiming to improve locomotion stability. To assess the stability and dynamic performance, a stable walking experiment with the exoskeleton was conducted, involving a male subject.

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Probabilistic Spiking Neural Network for Robotic Tactile Continual Learning

Probabilistic Spiking Neural Network for Robotic Tactile Con...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Senlin Fang Yiwen Liu Chengliang Liu Jingnan Wang Yuanzhe Su Yupo Zhang Hoiio Kong Zhengkun Yi Xinyu Wu City University of Macau Macau China The Department of Intelligent Systems and Robot Learning Lab ISRL Group SIAT Branch Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology Shenzhen

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual Learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.

关键词： Continuing education Measurement Uncertainty Spiking neural networks Learning (artificial intelligence) Benchmark testing Robot sensing systems

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6DoF-ICTC: A Deep Image Feature Extraction Network Combining Transformers and CNNs for 6DoF Pose Estimation 12

6DoF-ICTC: A Deep Image Feature Extraction Network Combining...

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12th International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zhang, Liming Zhou, Xin Zhu, Baixian Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In this work, we propose a new image feature extraction network for 6D pose estimation tasks. The majority of existing 6D pose estimation networks are based on the RGBD input, which is the fusion of images and point clouds. They are mainly working on keypoint extraction, point cloud feature extraction, and other tasks, however, image features, on the other hand, mainly rely on CNN networks that are currently advanced. With the development of Vision Transformer in the field of computer vision, the results on ImageNet demonstrate its benefit of extracting global features, and we see new directions that the Transformers can bring to the area of image feature extraction. ICTC is a Transformer paired with a CNN network that fuses the retrieved global features into each pixel and then does local feature extraction. This provides greater robustness to 6D pose estimation In complex situations such as shifting lighting conditions and object occlusion. Our method achieves 96% accuracy using the average ADD metric on the LineMOD dataset, demonstrating our method's effectiveness. © 2022 IEEE.

关键词： Feature extraction

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

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Procedia Computer Science 2024年 250卷 22-29页

作者： Hui Chen Chunjie Chen Zhuo Wang Fangliang Yang Heng Pan Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen 518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation.

关键词： Multimodal fusion terrain recognition vision transformer depth camera

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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World Rehabilitation Robot Convention (WRRC)

作者： Wujing Cao Xinqiang Guo Yupeng Zou Shuo Zhang Mingxiang Luo Worawarit Kobsiriphat Xinyu Wu Meng Yin Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering China University of Petroleum East China National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350355123

ISBN: (纸本)9798350355130

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance.

关键词： Support vector machines Deep learning Accuracy Noise Gesture recognition Stroke (medical condition) Feature extraction Electromyography Convolutional neural networks Long short term memory

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